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2,170 results for “clouds”
Data used in a manuscript: High sensitivity of cloud formation to aerosol changes
<p>Data used in a manuscript entitled: Revealing uncertainties in defining the sensitivity of cloud formation to aerosol changes</p> <p><strong>Puijo station datafiles (all data 1 hour averages)</strong></p> <p>Puijo_meteorological_data_1h_ave.csv</p> <ul> <li>Meteorological data measured in Puijo station</li> <li>Columns: Time, Visibility in m, Rain Intensity in mm/h, Temperature in C</li> </ul> <p>Puijo_DMPS_total_inlet_data_1h_ave.csv</p> <ul> <li>Particle size distributions measured via total inlet at Puijo station with DMPS system </li> <li>Columns: Time, total particle number concentration in #/cm3, particle concentrations at different size bins in #/cm3</li> <li>Size bin diameters in a Puijo_DMPS_Dp_nm_values.csv file </li> </ul> <p>Puijo_DMPS_interstitial_inlet_data_1h_ave.csv </p> <ul> <li>Particle size distributions measuresed via interstitia inlet at Puijo station with DMPS system </li> <li>Columns: Time, total particle number concentration in #/cm3, particle concentrations at different size bins in #/cm3</li> <li>Size bin diameters in a Puijo_DMPS_Dp_nm_values.csv file </li> </ul> <p>Puijo_DMPS_Dp_nm_values.csv</p> <ul> <li>Particle diameters for certain size bins in files Puijo_DMPS_total_inlet_data_1h_ave.csv and Puijo_DMPS_interstitial_inlet_data_1h_ave.csv</li> </ul> <p>Puijo_Forces2020_updraft_temp_data_1h_ave.csv</p> <ul> <li>updraft velocity and temperature data measured during Forces 2020 campaign</li> <li>Columns: time, updraft velocity in m/s (wwind), sigma wwind in m/s, r2 value, temperature in C</li> </ul> <p><strong>Pallas station datafiles (all data 1 hour averages)</strong></p> <p>Pallas_meteorological_data_1h_ave.csv</p> <ul> <li>Meteorological data measured in Pallas station</li> <li>Columns: Time, Visibility in m, Rain Intensity in mm/h, Temperature in C</li> </ul> <p>Pallas_DMPS_total_inlet_data_1h_ave.csv</p> <ul> <li>Particle size distributions measured via total inlet at Pallasstation with DMPS system </li> <li>Columns: Time, total particle number concentration in #/cm3, particle concentrations at different size bins in #/cm3</li> <li>Size bin diameters in a Puijo_DMPS_Dp_nm_values.csv file </li> </ul> <p>Pallas_DMPS_Dp_nm_values.csv</p> <ul> <li>Particle diameters for certain size bins in a file Pallas_DMPS_total_inlet_data_1h_ave.csv</li> </ul> <p><strong>Zeppelin station datafiles (all data 1 hour averages)</strong></p> <p>Zeppelin_updraft_temp_data_1h_ave.csv</p> <ul> <li>Anemometer measured in Zeppelin stations</li> <li>Columns: Time, wind speed in m/s (svel), temperature in C, sigma svel in m/s</li> </ul>
Contribution of surface and cloud radiative feedbacks to Greenland Ice Sheet meltwater production during 2002-2023
<p>These datasets accompany the paper titled "Contribution of surface and cloud radiative feedbacks to Greenland Ice Sheet meltwater production during 2002-2023". </p>
Data of Fluorescent Aerosol Particles during the Pallas Cloud Experiment 2022
<p>Overview: This data set contains number concentrations and number size distributions of total aerosol particles (TAPs), fluorescent aerosol particles (FAPs) and highly fluorescent aerosol particles (HFAPs) of diameter 0.5 to 30 µm (optical equivalent diameter) obtained by a Wideband Integrated Bioaerosol Sensor 5/NEO (Droplet Measurement Technologies) during the Pallas Cloud Experiment 2022 (PaCE22) campaign in the sub Arctic region of Finish Lapland. The instrument was operated between 17.09.2022 17:00 UTC and 13.12.2022 21:00 UTC at Sammaltunturi Station (67.9733°N 24.1157°E) at 564 m above sea level in the Pallas Atmosphere-Ecosystem Supersite and was connected to a heated total suspended particles (TSP) inlet.</p> <p>Instrument description: The WIBS samples air with a sample flow rate of 0.3 L/min and uses a 635 nm diode laser to measure the size of single particles between 0.5 and 30 µm and to trigger two xenon lamps with excitation wavelengths of 280 nm and 370 nm. Fluorescence emission intensity is measured in two emission windows of 310 - 400 nm and 420 - 650 nm resulting in three main fluorescence channels: FL1 (excitation 280 nm and emission 310 – 400 nm), FL2 (excitation 280 nm and emission 420 - 650 nm) and FL3 (excitation 370 nm and emission 420 - 650 nm). A particle is considered as fluorescent, if it exceeds the three sigma fluorescence threshold (forced trigger intensity plus three standard deviations) in any channel (FAP). A particle is considered highly fluorescent if it exceeds the nine sigma fluorescence threshold (forced trigger intensity plus nine standard deviations) in any channel (HFAP). Both FAPs and HFAPs are split into 10 types of fluorescent particles each, using the Perring nomenclature (Perring et al., 2015): (H)FAP_ A (FL1 only), (H)FAP_ B (FL2 only), (H)FAP_ C (FL3 only), (H)FAP_ AB (FL1 and FL2 only), (H)FAP_ AC (FL1 and FL3 only), (H)FAP_ BC(FL2 and FL3 only) and (H)FAP_ ABC (FL1 and FL2 and FL3), (H)FAP_ FL1 (A+AB+AC+ABC), (H)FAP_ FL2(B+AB+BC+BC) and (H)FAP_ FL3 (C+AC+BC+ABC). </p> <p>The files are separated by date (in UTC) and are named the following: TUW.WIBS_N.b1.yyyyMMdd.hhmm.csv. The data file processing level is b1. Quality control checks were applied and missing data points or those with bad values were set to -9999.9.</p> <p>References: Perring et al., “Airborne observations of regional variation in fluorescent aerosol across the United States,” J Geophys Res-Atmos, vol. 120, Art. no. 3, 2015.</p>
AT 2021hdr: a candidate tidal disruption of a gas cloud by a binary super massive black hole system
<p>In this dataset we provide tables for the Zwicky Transient Facility (ZTF) and Swift observations presented in the paper "AT 2021hdr: a candidate tidal disruption of a gas cloud by a binary super massive black hole system", accepted for publication in the Astronomy and Astrophysics Journal (Hernández-García et al. 2024). </p> <p> </p> <p>Files description:</p> <p>-ztf_table:_zenodo.csv: Log of the ZTF forced photometry observations. Include the dates in MJD, filter, apparent magnitudes, and difference fluxes.</p> <p>- swift_table_zenodo.csv: Log of the Swift observations, including the observation ID, date (in MJD), exposure time, count-rate in the 0.5-10 keV energy band, and UV fluxes in the UVW2 and UVM2 filters. </p>
Disequilibrium Chemistry, Diabatic Thermal Structure, and Clouds in the Atmosphere of COCONUTS-2b
<p>This dataset contains the Gemini/FLAMINGOS-2 near-infrared spectrum of the planetary-mass companion, COCONUTS-2b, as presented in the paper "Disequilibrium Chemistry, Diabatic Thermal Structure, and Clouds in the Atmosphere of COCONUTS-2b" (Zhang et al. 2024) for publication by Astronomical Journal (arXiv:2410.10939). This file includes three columns: wavelengths (unit: micron), flux (unit: erg/s/cm2/Angstrom), and flux uncertainties (unit: erg/s/cm2/Angstrom). Please cite this paper and the Zenodo repository if these data are used in your work.</p> <p> </p> <p> </p>
Data for: `Limits on planetary-mass primordial black holes from the OGLE high-cadence survey of the Magellanic Clouds'
<p>This repository contains data presented in the paper "Limits on planetary-mass primordial black holes from the OGLE high-cadence survey of the Magellanic Clouds".</p>
Data from: Trait‐based signatures of cloud base height in a tropical cloud forest
<p>Clouds have profound consequences for ecosystem structure and function. Yet, the direct monitoring of clouds and their effects on biota is challenging especially in remote and topographically complex tropical cloud forests. We argue that known relationships between climate and the taxonomic and functional composition of plant communities may provide a fingerprint of cloud base height, thus providing a rapid and cost-effective assessment in remote tropical cloud forests. To detect cloud base height, we compared species turnover and functional trait values among herbaceous and woody plant communities in an ecosystem dominated by cloud formation. We measured soil and air temperature, soil nutrient concentrations, and extracellular enzyme activity. We hypothesized that woody and herbaceous plants would provide signatures of cloud base height, as evidenced by abrupt shifts in both taxonomic composition and plant function. We demonstrated abrupt changes in taxonomic composition and the community-weighted mean of a key functional trait, specific leaf area, across elevation for both woody and herbaceous species, consistent with our predictions. However, abrupt taxonomic and functional changes occurred 100 m higher in elevation for herbaceous plants compared to woody ones. Soil temperature abruptly decreased where herbaceous taxonomic and functional turnover was high. Other environmental variables including soil biogeochemistry did not explain the abrupt change observed for woody plant communities. We provide evidence that a trait-based approach can be used to estimate cloud base height. We outline how rises in cloud base height and differential environmental requirements between growth forms can be distinguished using this approach.</p>
Recommendations for molecules of interest in the Taurus Molecular Cloud-1
<p>This dataset contains 1510 molecules of interest to the chemical inventory of TMC-1, recommended based on grounds of chemical similarity. These molecules were identified as part of work currently under review:</p> <p>Lee et al., "Machine Learning of Interstellar Chemical Inventories" (2021)</p> <p>Structures were generated from their SMILES strings using OpenBabel and rdkit, and geometry optimization carried out using the geomeTRIC program:</p> <p><br> Wang, L.‑P.; Song, C.C. (2016), J. Chem, Phys. 144, 214108. http://dx.doi.org/10.1063/1.4952956</p> <p><br> Electronic structure calculations were performed using psi4, with both geometry optimization and dipole moments calculated at at the 𝜔B97X‑D/6‑31+G(d) level of theory. Equilibrium dipole moments<br> and rotational constants are reported in unsigned debye and MHz respectively; for the latter, we provide effective scaled parameters as well that empirically correct for vibration‑rotation interactions. Please refer to “Lee, K. L. K. and McCarthy, M. 2020, J Phys Chem A, 5, 898” for information regarding their uncertainties. For molecules where SCF/geometry optimizations failed to converge, we provide their dipole moments based on the molecular mechanics structures. These molecules will be indicated by “Is DFT optimized?: False”.</p> <p><br> The predicted column densities and uncertainties are given with a simple Gaussian Process with rational quadratic and white noise kernels. Simply put, the predicted column densities of unseen molecules are given as functions of distance in chemical space that decays naturally to zero for infinite distance from other data points. The reader is encouraged to look at the distances between recommendations and TMC‑1 molecules to develop an intuition for how the predicted column density behaves roughly with distance, and interpret them with the uncertainties accordingly: as a guide but not to rule out molecules specifically. Molecules with particularly large uncertainties are<br> likely to be impactful in constraining the chemistry of the source, even if we provide just an upper limit.</p> <p><br> Finally, there is no real ordering of which the molecules are given. This is quasi‑random, although there are pockets of similar molecules based on how similar the TMC‑1 molecules are between searches.</p> <p> </p> <p>The included tarball contains the OpenBabel generated XYZ coordinates for each recommended species. This <a href="https://github.com/laserkelvin/umda">github repository</a> contains the notebooks and code used to generate this data.</p>
BSWO_cloud radar data
<p>Cloud radar data observed at BSWO between 2014 and 2017.</p> <p>cfradial files are identical to the netCDF files. </p> <p> </p>
Distinct Roles of Land Cover in Regulating Spatial Variabilities of Temperature Responses to Radiative Effects of Aerosols and Clouds
<p>Dataset of drawing figures in work 'Distinct Roles of Land Cover in Regulating Spatial Variabilities of Temperature Responses to Radiative Effects of Aerosols and Clouds'.</p> <p>Dataset File List </p> <p>1. Present_Day_Variables.nc : global spatial distributions of radiative forcing, climate sensitivity and temperature response </p> <p>2. Regional_Variables.xlsx : regional data of radiative forcing, climate sensitivity and temperature response</p> <p>3. Aerosol_Burdens.nc : monthly aerosol burdens (atmospheric column concentration) of ten simulation cases </p> <p>4. Aerosol_Burdens_README.txt : dimensional descriptions of Aerosol_Burdens.nc</p> <p>5. Monthly_Radiation.nc : monthly radiation variables of PD simulation for testing model performance</p> <p>6. Monthly_Radiation_README.txt : dimensional descriptions of Monthly_Radiation.nc</p> <p> </p>
The TELPERION Survey for Distant [O III] Clouds Around Luminous and Hibernating AGN
<p>The collection includes the FITS files of newly-obtained data used for the study “The TELPERION Survey for Distant [O III] Clouds Around Luminous and Hibernating AGN” by William C. Keel et al., submitted to Monthly Notices of the Royal Astronomical Society.</p> <p>Zip files for objects in the VCV sample (and the best-observed in the Toomre merger <br> sample) use object names as in the tablesin the manuscript. File names use these conventions:<br> Suffix o3.fits : narrowband F510 filter including redshifted [O III] emission<br> Suffix o3co.fits: as above with WCS information from the astrometry.net web service<br> Suffix v.fits: in the Johnson V band<br> Suffix vco.fits: V band, with WCS coordinate metadata<br> Suffix v2o3.fits: V image resampled to the same WCS coordinate grid as the F510 image, when the two are from different cameras and not related by a simple shift.<br> vscale.fits: resampled V image scaled for continuum subtraction from the F510 data.<br> vs.fits: as above, with small shifts accounting for asymmetric point-spread functions (PSFs).<br> vg.fits, vsg.fits: as above images, with Gaussian blur for first-order accounting of PSF differences.<br> diff.fits - difference image nominally representing pure [O III] emission. There may be a version numberb(diff2, diff3) if multiple iterations were done. These may also have names such as diff2g.fits, diff1s.fits if Gaussian blur or small shifts were done as for vs.fits, vsg.fits files.</p> <p>"flat" denotes that a low-pass spatial filter (such as a large median) has been used to reduce the effects of stray light on a difference image.</p> <p>Toomre.zip includes F510 and V images for those galaxies in the Toomre-sequence sample not included in the SARA data.</p> <p>LongSlit.zip includes reduced two-dimensional spectral data as detailed in the paper. _abs indicates that the data values are calibrated to intensity units (ergs/(cm^2 s Angstrom)) using standard-star observations; otherwise they are in instrumental units. Wavelengths are in Angstroms, in air. Data using the ADAM instrument are prefixed with that string.</p> <p>Tunable.zip includes the results of the MaNGAL and SCORPIO tunable-filter observations. The string “5007net” denotes that the continuum (“cont”) has been subtracted; “5007” by itself marks images including the [O III] line but not yet continuum-subtracted.</p>
Figure 5. Phyllophaga sechi n in Two new species of Phyllophaga of the Schizorhina species group from cloud forests of Chiapas, México and Guatemala (Coleoptera: Scarabaeidae: Melolonthinae)
Figure 5. Phyllophaga sechi n. sp. A) Dorsal habitus. B) Lateral habitus.
Figure 1. Phyllophaga badbunnyi n in Two new species of Phyllophaga of the Schizorhina species group from cloud forests of Chiapas, México and Guatemala (Coleoptera: Scarabaeidae: Melolonthinae)
Figure 1. Phyllophaga badbunnyi n. sp. A) Dorsal habitus. B) Lateral habitus.
WHUS2-CD+ dataset for sentinel-2 cloud detection validation
<p>WHUS2-CD+ is a cloud validation detection dataset for Sentinel-2A images. WHUS2-CD+ contains 36 manually labeled cloud masks at 10m resolution and corresponding Sentinel-2A images evenly distributed over China mainland.</p> <p>If you use this dataset for your research, please cite us accordingly:</p> <p>#Reference: </p> <p>[1] J. Li, Z. Wu, Z. Hu, C. Jian, S. Luo, L. Mou, X. Zhu, and M. Molinier, "A lightweight deep learning based cloud detection method for Sentinel-2A imagery fusing multi-scale spectral and spatial features," in IEEE Transactions on Geoscience and Remote Sensing, 2021. <a href="https://doi.org/10.1109/TGRS.2021.3069641">https://doi.org/10.1109/TGRS.2021.3069641</a>.</p> <p>[2] Z. Wu, J. Li, Y. Wang, Z. Hu and M. Molinier, "Self-Attentive Generative Adversarial Network for Cloud Detection in High Resolution Remote Sensing Images," in IEEE Geoscience and Remote Sensing Letters, vol. 17, no. 10, pp. 1792-1796, Oct. 2020. <a href="https://doi.org/10.1109/LGRS.2019.2955071">https://doi.org/10.1109/LGRS.2019.2955071</a>.</p> <p>The training and testing list is (The challenging senes are marked in bold):</p> <table> <tbody> <tr> <td>Training set</td> </tr> <tr> <td>S2A_MSIL1C_20190714T043711_N0208_R033_T46TFN_20190714T073938</td> </tr> <tr> <td>S2A_MSIL1C_20191219T040151_N0208_R004_T47SQU_20191219T055033</td> </tr> <tr> <td>S2A_MSIL1C_20190630T045701_N0207_R119_T45SWC_20190630T080543</td> </tr> <tr> <td>S2A_MSIL1C_20191215T042151_N0208_R090_T46RGV_20191215T065406</td> </tr> <tr> <td>S2A_MSIL1C_20180930T044701_N0206_R076_T45SXR_20180930T074413</td> </tr> <tr> <td>S2A_MSIL1C_20200317T024541_N0209_R132_T51TWM_20200317T053350</td> </tr> <tr> <td>S2A_MSIL1C_20180816T053641_N0206_R005_T44TKK_20180816T093424</td> </tr> <tr> <td>S2A_MSIL1C_20191023T040821_N0208_R047_T47TQF_20191023T074550</td> </tr> <tr> <td>S2A_MSIL1C_20180824T031541_N0206_R118_T50TKL_20180824T061636</td> </tr> <tr> <td>S2A_MSIL1C_20191118T025011_N0208_R132_T50RMN_20191118T071843</td> </tr> <tr> <td>S2A_MSIL1C_20190916T023551_N0208_R089_T50RQS_20190916T042547</td> </tr> <tr> <td>S2A_MSIL1C_20190819T031541_N0208_R118_T49SFU_20190819T065332</td> </tr> <tr> <td>S2A_MSIL1C_20190815T051651_N0208_R062_T44TPN_20190815T090034</td> </tr> <tr> <td>S2A_MSIL1C_20200410T022551_N0209_R046_T51TXG_20200410T042047</td> </tr> <tr> <td>S2A_MSIL1C_20191002T025551_N0208_R032_T50TQQ_20191002T054113</td> </tr> <tr> <td>S2A_MSIL1C_20180429T032541_N0206_R018_T49SCV_20180429T062304</td> </tr> <tr> <td>S2A_MSIL1C_20200506T024551_N0209_R132_T51UWS_20200506T043639</td> </tr> <tr> <td>S2A_MSIL1C_20200325T034531_N0209_R104_T47RQL_20200325T065315</td> </tr> <tr> <td>S2A_MSIL1C_20190928T031541_N0208_R118_T49RBJ_20190928T061248</td> </tr> <tr> <td>S2A_MSIL1C_20180827T032541_N0206_R018_T48RYV_20180827T062627</td> </tr> <tr> <td>S2A_MSIL1C_20200222T030731_N0209_R075_T49QEE_20200222T060244</td> </tr> <tr> <td>S2A_MSIL1C_20180722T030541_N0206_R075_T49RFP_20180722T060550</td> </tr> <tr> <td>S2A_MSIL1C_20180729T025551_N0206_R032_T49RGL_20180729T055945</td> </tr> <tr> <td>S2A_MSIL1C_20200506T024551_N0209_R132_T50SPE_20200506T052918</td> </tr> <tr> <td>Testing set</td> </tr> <tr> <td>S2A_MSIL1C_20180930T030541_N0206_R075_T49QDD_20180930T060706</td> </tr> <tr> <td>S2A_MSIL1C_20191105T023901_N0208_R089_T51STR_20191105T054744</td> </tr> <tr> <td>S2A_MSIL1C_20190812T032541_N0208_R018_T48RXU_20190812T070322</td> </tr> <tr> <td>S2A_MSIL1C_20190602T021611_N0207_R003_T52TES_20190602T042019</td> </tr> <tr> <td>S2A_MSIL1C_20190328T033701_N0207_R061_T49TCF_20190328T071457</td> </tr> <tr> <td>S2A_MSIL1C_20191001T050701_N0208_R019_T45TXN_20191002T142939</td> </tr> <tr> <td>S2A_MSIL1C_20200416T042701_N0209_R133_T46SFE_20200416T074050</td> </tr> <tr> <td>S2A_MSIL1C_20200528T050701_N0209_R019_T44SPC_20200528T082127</td> </tr> <tr> <td><strong>S2A_MSIL1C_20210207T023851_N0209_R089_T52UCU_20210207T040210</strong></td> </tr> <tr> <td><strong>S2A_MSIL1C_20210126T052111_N0209_R062_T44SNE_20210126T063836</strong></td> </tr> <tr> <td><strong>S2A_MSIL1C_20210102T054231_N0209_R005_T43SFB_20210102T065941</strong></td> </tr> <tr> <td><strong>S2A_MSIL1C_20201206T041141_N0209_R047_T47SMV_20201206T053320</strong></td> </tr> </tbody> </table>
Aerosol–cloud interactions: the representation of heterogeneous ice activation in cloud models
<p>The files contain the data sets shown in the publication "Aerosol–cloud interactions: the representation of heterogeneous ice activation in cloud models" to appear in final form in the journal Atmos. Chem. Phys. in 2021. A preliminary discussion version of the manuscript is available under https://doi.org/10.5194/acp-21-1-2021.</p> <p>The files include a power point slide containing the schematic Fig.1 and xmgrace plot files containing the research data (ASCII) shown in Figs.2,3,4, and A1. Specifically (Gn means graph #n, Sm means set # m):</p> <p>in Fig.2, the black solid curve is listed under G0.S0; the blue solid curve G0.S1; dashed = G0.S2; dot-dashed = G0.S3</p> <p>in Fig.3, curve (a) = G0.S0; black curve (b) = G1.S0, red curve (b) = G1.S1; black solid (c) = G2.S0 black dashed (c) = G2.S3; red solid (c) = G2.S1, red dashed (c) = G2.S2; curve (d) = G3.S0, black curve (e) = G4.S0; red curve (e) = G4.S1, black solid (f) = G5.S0, black dashed (f) = G5.S4, red solid (f) = G5.S1, red dashed (f) = G5.S2</p> <p>in Fig.4, solid orange curve (a) = G0.S0, dashed orange (a) = G0.S1, solid red (a) G0.S2, dashed red (a) = G0.S3, solid blue (a) = G0.S4, dashed blue (a) = G0.S5; solid curve (b) = G1.S0, dashed (b) = G1.S1</p> <p>in Fig.A1, solid black curve = G0.S0, solid blue = G0.S1</p>
Impact of 3D Cloud Structures on the Atmospheric Trace Gas Products from UV-VIS Sounders: Synthetic dataset for validation of trace gas retrieval algorithms
<p>This data set is described in detail in a paper submitted to AMTD:</p> <p><strong>Impact of 3D Cloud Structures on the Atmospheric Trace Gas Products from UV-VIS Sounders - Part I: Synthetic dataset for validation of trace gas retrieval algorithms</strong></p> <p>by Claudia Emde, Huan Yu, Arve Kylling, Michel van Roozendael, Kerstin Stebel, Ben Veihelmann, and<br> Bernhard Mayer</p> <p> </p> <p>The subdirectory <em>boxcloud</em> includes synthetic reflectances for clearsky, 1D cloud and box cloud.</p> <p>The subdirectory <em>les_cloud</em> includes synthetic reflectances for the LES cloud scenario for low earth orbit (<em>leo</em>) and geostationary orbit (<em>geo</em>).</p> <p>All data are provided in <em>netcdf</em> format.</p> <p> </p>
Photorealistic and classified urban point cloud dataset (Helsinki)
<p>The dataset contains a photorealistic and classified urban point cloud from Kalasatama region in Helsinki, Finland. Data was created using both terrestrial laser scanning (Leica RTC360) and UAV-based (DJI P4 Pro+) photogrammetry. 3D reconstruction was completed with RealityCapture and point cloud classification with TerraScan. The dataset is georeferenced in ETRS-TM35FIN (EPSG 3067) coordinate system. Work was done in Aalto University (The Research Institute of Measuring and Modeling for the Built Environment) with support from the City of Helsinki.</p>
CN, Vehicles Platooning, Cloud-assisted platooning
<p>Use Case Category: <strong>Vehicles Platooning</strong><br> User Story: <strong>Cloud-assisted platooning</strong><br> Location: Chinese (CN) trial site</p> <p>According to 3GPP TS 22.186 R16, Vehicles Platooning “enables the vehicles to dynamically form a group travelling together. All the vehicles in the platoon receive periodic data from the leading vehicle, in order to carry on platoon operations. This information allows the distance between vehicles to become extremely small, i.e., the gap distance translated to time can be very low (sub second). Platooning applications may allow the vehicles following to be autonomously driven”.</p> <p>User Story: <strong>Cloud-assisted platooning</strong></p> <p>The autonomous driving vehicle fleet communicates with each other through LTE-V at the start. Among them, the leading vehicle includes the platoon control unit (PCU), which coordinates the vehicles in the fleet to ensure a certain safe distance and to drive in a platoon. The leading vehicle communicates with the control centre deployed in a cloud server through V2N to obtain the test scheme and the global path planning. Then it provides the basic planning for the rear vehicle through V2V communication (including chasing, continuous running, acceleration, deceleration, obstacle avoidance, overall acceleration, and deceleration, etc.). The following vehicle also has a certain perception and planning decision-making ability. Besides, LTE-V communication can be replaced by DSRC technology, and comparison between these two methods will be implemented.</p>
CN, Advanced Driving, Cloud-assisted advanced driving
<p>Use Case Category: <strong>Advanced Driving</strong><br> User Story: <strong>Cloud-assisted advanced driving</strong><br> Location: Chinese (CN) trial site</p> <p>According to 3GPP TS 22.186 R16, Advanced Driving “enables semi-automated or fully-automated driving. Longer inter-vehicle distance is assumed. Each vehicle and/or Road Side Unit (RSU) shares data obtained from its local sensors with vehicles in proximity, thus allowing vehicles to coordinate their trajectories or manoeuvres. In addition, each vehicle shares its driving intention with vehicles in proximity. The benefits of this use case group are safer traveling, collision avoidance, and improved traffic efficiency”.</p> <p>User Story: Cloud-assisted advanced driving</p> <p>In the China site, the roadside unit, remote control centre and cloud server will monitor and control the autonomous vehicles in real time, to realize the various testes of Internet-connected applications of vehicles safely and efficiently. In the high-level abstraction of the user story, Vehicle A and Vehicle B are both running in a straight line along the right lane. Vehicle B is traveling at a constant speed in front of Vehicle A, and both vehicles keep a small distance. In the beginning, the remote-control centre (MEC) issues the overtaking order to vehicle A through the RSU. After receiving the order, Vehicle A sends the overtaking information to Vehicle B through V2V communication and receive the real-time information from Vehicle B via V2V, which includes the position, speed and heading angle of the vehicle. Thus, Vehicle A makes an automatic decision according to the information of Vehicle B.</p> <p>The decision-making of Vehicle A is detailed as follows: First, it changes to the left lane; then it accelerates to overtake according to the speed of Vehicle B. Finally, it changes lane back to the right lane, and sends its information to Vehicle B. In this way, these two cars complete overtaking operation. In this process, Vehicle A exchanges messages related to CoCA (Coordinated collision avoidance) through the 3GPP V2X communication service, while other vehicles transmit messages through the 3GPP communication service as well, finally they will achieve coordinated manoeuvring.</p>
SAGE Sky Images and model checkpoints for "Let's Unleash the Network Judgement: A Self-supervised Approach for Cloud Image Analysis" paper
<p>This dataset contains:</p> <ul> <li>84912 images collected from our camera (a 6MP Fisheye Sky facing camera with 192 degrees horizontal and vertical angle view). Each image is a JPEG image, JFIF standard 1.01, aspect ratio, density 1x1, segment length 16, baseline, precision 8, 2048x2048, components 3. Images were recorded on October–December 2021. The dataset is clusterized with images belonging to 20 different clusters/classes. We used PCA_MLE plus SOM to produce such clusterization. Files Cluster_*.zip</li> <li>11 model checkpoints from the training process. Files checkpoint*.pth and log.txt</li> <li>Output feature vectors and their corresponding file names resulting from inference . File features.zip</li> <li>The clusterization produced on <a href="https://github.com/CV-Application/WSISEG-Database ">WSISEG-Database</a>. File README.txt </li> </ul>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.